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This paper aims to define the class of fragment constructive heuristics used to compute feasible solutions for the traveling salesman problem (TSP) into edge-greedy and vertex-greedy subclasses. As these subclasses of heuristics can create subtours, two known methodologies for subtour elimination on symmetric instances are reviewed and are expanded to cover asymmetric problem instances. This paper introduces a third novel subtour elimination methodology, the greedy tracker (GT), and compares it to both known methodologies.

Computational results for all three subtour elimination methodologies are generated across 17 symmetric instances ranging in size from 29 vertices to 5,934 vertices, as well as 9 asymmetric instances ranging in size from 17 to 443 vertices.

The results demonstrate the GT is the fastest method for preventing subtours for instances below 400 vertices. Additionally, a distinction between fragment constructive heuristics and the subtour elimination methodology used to ensure the feasibility of resulting solutions enables the introduction of a new vertex-greedy fragment heuristic called ordered greedy.

This research has two main contributions: first, it introduces a novel subtour elimination methodology. Second, the research introduces the concept of ordered lists which remaps the TSP into a new space with promising initial computational results.

Because much human knowledge consists of elementary fragments of know‐how, applying a significant amount of knowledge requires new ways to organize decision‐making fragments into competent wholes. Knowledge systems collect these fragments in a knowledge base and then access the knowledge base to reason about each specific problem. As a consequence, knowledge systems differ from conventional programs in the way they're organized, the way they incorporate knowledge, the way they execute, and the impression they create through their interactions. Knowledge systems simulate expert human performance, and they present a humanlike facade to the user.

Despite its volume, the academic research on the impact of electronic word of mouth (eWOM) on tourism and hospitality consumer behaviour is fragmented and largely limited to investigating a small scope of its impact. The purpose of this paper is to bridge this gap by synthesising the existing literature, providing a conceptual framework for the various dimensions of this impact.

This paper is based on the meta-synthesis and a critical conceptual analysis of relevant academic literature identified using a keyword search of papers via the Web of Science and Scopus databases, followed by a snowballing process comprising tracking the citations to the resources referred to in the identified papers.

This conceptual analysis illustrates how the full spectrum of the impact of eWOM on tourist behaviour spans well beyond the limited scope traditionally focussed on by tourism and hospitality researchers. This scope, encompassing multiple cognitive, normative and affective dimensions, is illustrated in an evidence-based conceptual framework proposed in this paper, providing a systematic tool to identify the less-studied aspects of this important phenomenon.

This paper synthesises the large and fragmented body of literature on eWOM and proposes a novel conceptual framework, illustrating the vast scope of the various cognitive, affective and normative mechanisms through which eWOM affects consumers’ choice of tourism and hospitality products. Furthermore, this paper provides a synthesis of the state-of-the-art of research in this field, highlights the existing gaps and provides researchers with a systematic tool to identify pathways towards breaking the status-quo in progressing beyond the current boundaries of academic research in this field.

游客感知对清真旅游目的地的影响：结构模型分析

尽管有大量研究, 但有关网络口碑eWOM对旅游业和酒店业消费者行为的影响的学术研究仍是零散的, 并且在很大程度上只限于调查其影响的一小部分。本文的目的是通过综合现有文献来弥合这种差距, 为这种影响的各个方面提供概念框架。

本文基于元合成法和对相关学术文献的关键性, 概念性分析。这些文献是通过Web of Science和Scopus数据库对文章进行关键字搜索而确定的。随后是滚雪球式搜索过程, 该过程包括跟踪对已识别文章中引用的资源的引用。

这项概念分析阐述了eWOM对游客行为的影响范围如何远远超出了旅游和酒店业研究人员传统上关注的有限范围。这一范围是基于有理论依据的的概念框架提出的, 并涵盖多个认知, 规范和情感的维度。此范围为识别这一重要且缺乏研究的现象提供了系统的工具。

本文综合了有关eWOM的大量零散文献, 并提出了一个新颖的概念框架, 阐明了eWOM影响消费者选择旅游和酒店产品的各种认知, 情感和规范机制的范围。此外, 本文提供了该领域最新的研究综述, 突出了现有的差距, 并为研究人员提供了系统的识别打破现状的途径, 以超越当前学术界在这个领域的研究局限。

关键字 电子口碑, eWOM, WOM, 酒店, 旅游, 口碑

研究论文

El Boca a Boca Electrónico en el Comportamiento del Consumidor Turístico y Hotelero: Estado del Arte

A pesar del gran volumen de investigación académica sobre el impacto de eWOM en el comportamiento del consumidor turístico y hotelero, esta se encuentra fragmentada y en gran medida se limita a investigar un pequeño alcance de su impacto. El propósito de este artículo, es cerrar esta brecha sintetizando la literatura existente y proporcionando un marco conceptual para las diversas dimensiones de este impacto.

Este documento se basa en un análisis metasíntesis crítico y conceptual de la literatura relevante académica identificada, mediante una búsqueda de artículos por palabras claves a través de las bases de datos de Web of Science y Scopus, seguido de un proceso de “bola de nieve” que comprende el seguimiento de las citas de los recursos mencionados en los artículos identificados.

Este análisis conceptual, ilustra cómo el espectro total del impacto de eWOM en el comportamiento turístico se extiende mucho más allá del limitado alcance que tradicionalmente han realizado investigadores del turismo y hoteles. Este nuevo alcance, que abarca múltiples dimensiones cognitivas, normativas y afectivas, se ilustra en un marco conceptual basado en la evidencia propuesta en este documento, que proporciona una herramienta sistemática para identificar los aspectos menos estudiados de este importante fenómeno.

Este documento sintetiza la gran cantidad de literatura fragmentada sobre eWOM y propone un marco conceptual novedoso, que ilustra el amplio alcance de los diversos mecanismos cognitivos, afectivos y normativos a través de los cuales eWOM afecta la elección de los consumidores de productos turísticos y hoteleros. Además, este documento proporciona una síntesis del estado de la investigación en este campo, destaca las brechas existentes y brinda a los investigadores una herramienta sistemática para identificar vías para romper el statu quo en el progreso más allá de los límites académicos actuales en este campo.

Boca a boca electrónica, eWOM, WOM, Industria hoteleram, Turismo, Boca a boca

– Trabajo de investigación

The purpose of this research is to develop a time-cost optimization model to schedule repetitive projects while considering limited resource availability.

The model is based on the constraint programming (CP) framework; it integrates multiple scheduling characteristics of repetitive activities such as continuous or fragmented execution, atypical activities and coexistence of different modes in an activity. To improve project performance while avoiding inefficient hiring and firing conditions, the strategy of bidirectional acceleration is presented and implemented, which requires keeping regular changes in the execution modes between successive subactivities in the same activity.

Two case studies involving a real residential building construction project and a hotel refurbishing project are used to demonstrate the application of the proposed model based on four different scenarios. The results show that (1) the CP model has great advantages in terms of solving speed and solution quality than its equivalent mathematical model, (2) higher project performance can be obtained compared to using previously developed models and (3) the model can be easily replicated or even modified to enable multicrew implementation.

The original contribution of this research is presenting a novel CP-based repetitive scheduling optimization model to solve the multimode resource-constrained time-cost tradeoff problem of repetitive projects. The model has the capability of minimizing the project total cost that is composed of direct costs, indirect costs, early completion incentives and late completion penalties.

Restructuring a business process (BP) model may enhance the BP performance and improve its understandability. So-far proposed restructuring methods use either refactoring which focuses on structural aspects, social network discovery which uses semantic information to guide the affiliation process during its analysis, or social network rediscovery which uses structural information to identify clusters of actors according to their relationships. The purpose of this paper is to propose a hybrid method that exploits both the semantic and structural aspects of a BP model.

The proposed method first generates a social network from the BP model. Second, it applies hierarchical clustering to determine the performers’ partitions; this step uses the social context which specifies features related to performers, and two new distances that account for semantic and structural information. Finally, it applies a set of behavioral and organizational restructuring rules adapted from the graph optimization domain; each rule uses the identified performers’ partitions and the business context to reduce particular quality metrics.

The efficiency of the proposed method is illustrated through well-established complexity metrics. The illustration is made through the development of a tool that fully supports the proposed method and proposes a strategy for the application of the restructuring rules.

The proposed method has the merit of combining the semantic and structural aspects of a Business Process Modeling Notation model to identify restructuring operations whose ordered application reduces the complexity of the initial model.

This paper aims to develop a conceptual framework on how the representativeness heuristic operates in the decision-making process. Specifically, the authors unbundle representativeness into its building blocks: search rule, stopping rule and decision rule. Furthermore, the focus is placed on how individual-level cognitive and behavioral factors, namely experience, intuition and overconfidence, affect the functioning of this heuristic.

From a theoretical standpoint, the authors build on dual-process theories and on the adaptive toolbox view from the “fast and frugal heuristics” perspective to develop an integrative conceptual framework that uncovers the mechanisms underlying the representativeness heuristic.

The authors’ conceptualization suggests that the search rule used in representativeness is based on analogical mapping from previous experience, the stopping rule is the representational stability of the analogs and the decision rule is the choice of the alternative upon which there is a convergence of representations and that exceeds the decision maker's aspiration level. In this framework, intuition may help the decision maker to cross-map potentially competing analogies, while overconfidence affects the search time and costs and alters both the stopping and the decision rule.

The authors develop a conceptual framework on representativeness, as one of the most common, though still poorly investigated, heuristics. The model offers a nuanced perspective that explores the cognitive and behavioral mechanisms that shape the use of representativeness in decision-making. The authors also discuss the theoretical implications of their model and outline future research avenues that may further contribute to enriching their understanding of decision-making processes.

The continued evolution of influencer marketing has created a need to better understand influencer marketing effectiveness. With brands increasingly partnering with influencers, research is yet to provide an integrated perspective examining the critical role of both parties. This study aims to draw on the source credibility model and signaling theory to explain the mechanisms that matter in influencer marketing effectiveness.

The proposed model of influencer marketing effectiveness is analyzed using partial least squares with data from 281 followers of social media influencers.

The authors establish influencer characteristics of popularity and attractiveness as heuristic cues that inform judgments of influencer efficacy. Further, category involvement and altruistic motives for collaboration are shown to moderate followers’ reliance on these heuristic cues. Then, a sequential mediating effect demonstrates the critical roles of the influencer and partner brand in three desired outcomes: enhanced perception of brand authenticity, enhanced brand engagement and positive attitudes toward influencer posts.

Future research should consider other heuristic cues that could inform influencer efficacy judgments and switch the focus toward the partner brand’s impact on such judgments.

A step-by-step visual framework is presented to help marketers and influencers translate these findings into key responsibilities for developing more effective and collaborative partnerships.

Besides presenting an integrated perspective, signaling theory provides an original lens for explaining influencer marketing effectiveness, addressing the need to expand the theoretical boundaries of influencer marketing research. 

A web site usually contains a large number of concept entities, each consisting of one or more web pages connected by hyperlinks. In order to discover these concept entities for more expressive web site queries and other applications, the web unit mining problem has been proposed. Web unit mining aims to determine web pages that constitute a concept entity and classify concept entities into categories. Nevertheless, the performance of an existing web unit mining algorithm, iWUM, suffers as it may create more than one web unit (incomplete web units) from a single concept entity. This paper presents two methods to solve this problem. The first method introduces a more effective web fragment construction method so as reduce later classification errors. The second method incorporates site‐specific knowledge to discover and handle incomplete web units. Experiments show that incomplete web units can be removed and overall accuracy has been significantly improved, especially on the precision and F1 measures.

The cost-oriented robotic assembly line balancing problem (cRALBP) has practical importance in real-life manufacturing scenarios. However, only a few studies tackle the cRALBP using exact methods or metaheuristics. This paper aims to propose a hybrid particle swarm optimization (PSO) combined with dynamic programming (DPPSO) to solve cRALBP type-I.

Two different encoding schemes are presented for comparison. In the frequently used Scheme 1, a full encoding of task permutations and robot allocations is adopted, and a relatively large search space is generated. DPSO1 and DPSO2 with the full encoding scheme are developed. To reduce the search space and concern promising solution regions, in Scheme 2, only task permutations are encoded, and DP is used to obtain the optimal robot sequence for a given task permutation in a polynomial time. DPPSO is proposed.

A set of instances is generated, and the numerical experiments indicate that DPPSO achieves a tradeoff between solution quality and computation time and outperforms existing algorithms in solution quality.

The contributions of this paper are three aspects. First, two different schemes of encoding are presented, and three PSO algorithms are developed for the purpose of comparison. Second, a novel updating mechanism of discrete PSO is adjusted to generate feasible task permutations for cRALBP. Finally, a set of instances is generated based on two cost parameters, then the performances of algorithms are systematically compared.

After considering the search process and functions of index entries, a classification of entry types is offered, based on index term context, predominant term order, and between‐term function words. Then a multiple entry generation scheme is described, comprising rules for term manipulation, input and output. After discussing access points and cross reference measures, a preliminary linguistic analysis is given, showing links with psycholinguistics. The study forms the basis of a current laboratory investigation (EPSILON) into a number of entry types.